A lab-based test setup was developed to simulate a novel droop rate controlled DC bus charging plaza installation in the Netherlands. The system consists of multiple bidirectional DC charging points, a PV array and a bidirectional grid connection. Currently the installed system employs linear droop control at the charge points and active grid connection. This lab setup allows for the testing of new control schemes, such as piecewise linear droop control, before implementing in the installed system. The simulations performed in this study investigate a variety of power flow scenarios and determine appropriate voltage and current setpoints and control mechanisms.
The aim of this constructive study was to develop model-based principles to provide guidance to managers and policy makers when making decisions about team size and composition in the context of home healthcare. Six model-based principles were developed based on extensive data analysis and in close interaction with practice. In particular, the principles involve insights in capacity planning, travel time, available effective capacity, contract types, and team manageability. The principles are formalized in terms of elementary mathematical models that capture the essence of decision-making. Numerical results based on real-life scenarios reveal that efficiency improves with team size, albeit more prominently for smaller teams due to diminishing returns. Moreover, it is demonstrated that the complexity of managing and coordinating a team becomes increasingly more difficult as team size grows. An estimate for travel time is provided given the size and territory of a team, as well as an upper bound for the fraction of full-time contracts, if split shifts are to be avoided. Overall, it can be concluded that an ideally sized team should serve (at least) around a few hundreds care hours per week.
Sustainability transition research seeks to understand the patterns and dynamics of structural societal change as well as unearth strategies for governance. However, existing frameworks emphasize innovation and build-up over exnovation and break-down. This limits their potential in making sense of the turbulent and chaotic dynamics of current transition-in-the-making. Addressing this gap, our paper elaborates on the development and use of the X-curve framework. The X-curve provides a simplified depiction of transitions that explicitly captures the patterns of build-up, breakdown, and their interactions.Using three cases, we illustrate the X-curve’s main strength as a framework that can support groups of people to develop a shared understanding of the dynamics in transitions-in-the-making. This helps them reflect upon their roles, potential influence, and the needed capacities for desired transitions. We discuss some challenges in using the X-curve framework, such as participants’ grasp of ‘chaos’, and provide suggestions on how to address these challenges and strengthen the frameworks’ ability to support understanding and navigation of transition dynamics. We conclude by summarizing its main strength and invite the reader to use it, reflect on it, build on it, and judge its value for action research on sustainability transitions themselves.
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